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Rescue
Your developer left in month three. Your Cursor-built chatbot works in testing but breaks in production. Your RAG prototype answers 60% of questions correctly and you don't know why it fails on the other 40%. Your vendor demo-ed something impressive but the code they handed over doesn't run.
We take over stalled AI projects. That means reading someone else's code, running it, measuring what works and what doesn't, and either fixing it or telling you clearly why it should be rebuilt.
Tell us what broke.
Most failed AI projects fall into one of five patterns. Knowing which pattern you're in determines the right fix.
Built fast with an AI coding tool over a weekend or a sprint. Works perfectly on the happy-path inputs in the demo. Falls apart on anything slightly outside that narrow set: different date formats, longer inputs, non-English queries, edge cases the builder didn't think of. There are no tests, no evals, no error handling beyond a generic try/catch. It passed the stakeholder demo, then went into production and started failing silently. Typical fix: add an eval harness, identify the ten most common failure cases by running the system on real production inputs, fix them systematically with regression tests that prevent them from recurring.
An agency shipped v1, took the final payment, and went quiet. The system runs (barely) but nobody inside your company understands how. There is no documentation, no runbook, no architecture diagram. Monitoring alerts exist but point to an email address nobody checks. If a prompt changes on the model provider's end, nobody will know until users start complaining. Typical fix: a full audit pass to document what the system actually does, adding structured logging and monitoring alerts that go to people who will act on them, and either a handoff to your internal team with documentation or a transition to an ongoing maintenance engagement.
You built a product that integrated a third-party AI API, a model provider, a retrieval service, an embeddings endpoint. That third party changed their schema, deprecated an endpoint, silently updated their model's behavior, or switched from one tokenizer to another. Your prompts no longer produce the output they used to. The failure is subtle enough that it passed automated tests but wrong enough that users notice. Typical fix: audit prompt performance against the current model behavior by running your golden-set test queries and comparing output against expected results. Rewrite affected prompts, update the integration layer, and add regression tests that catch future model drift before users do.
Started as a simple FAQ bot that answered three types of questions from a 20-page document. Grew (without a proper redesign) to answer complex multi-part questions drawing from a corpus of 500 documents, including contradictory policy updates. The retrieval strategy that worked for the original scope now returns conflicting chunks. The LLM hallucinates on 30% of responses because it's being asked to synthesize things that can't be synthesized cleanly. Everyone knows it's broken but the fix keeps getting deferred. Typical fix: scope back explicitly to what the system is actually good at, add confidence scoring so low-confidence answers don't reach users, and build fallback paths, either a human handoff or an explicit 'I don't know' response, for queries outside the reliable scope.
The engineer who built the system left the company. What they left behind is a Python script or a half-finished LangChain implementation in a repository with three commits, no README, and no tests. It runs on a specific version of Python with dependencies pinned to last year. The prompt strings are hardcoded in six different files. Nobody knows what the system does until they run it and watch what comes out. Typical fix: read the code, run it in a safe environment, write characterization tests around the behavior that's currently working, then refactor incrementally with tests as the safety net so every change is verifiable.
Every rescue starts with a diagnostic. Nothing gets fixed before we understand what's broken and why.
Week 1
We run your system, document what's working and what's broken, and trace failures back to their root cause. At the end of week 1 you receive a written assessment: what the system does, where it fails, and three paths forward (targeted fix, partial rebuild, or full rebuild) with honest cost estimates for each. We don't recommend the most expensive option by default. We recommend the option that makes the most sense for the failure pattern we found.
Week 2
You review the assessment, ask questions, and pick a path. If we recommend a full rebuild and you want spot fixes instead, we'll explain the tradeoffs clearly, but we'll do what you decide. The decision is yours. If you want to proceed, we write a fixed-range quote. No open-ended retainers, no hourly billing that runs past the estimate.
Weeks 3–8
Depending on the path chosen, we either fix the identified failure modes in the existing system or rebuild from the broken foundation. Weekly demos throughout so you can see progress and redirect early if priorities shift. We don't go heads-down for six weeks and surface at the end with a surprise.
Handoff
A working system, documented architecture, an eval suite that measures the things that were previously breaking, monitoring configured to catch regressions, and a runbook your team can actually use. If you want to extend the system after handoff, you have everything you need to do that without us.
Rescue work has more unknowns than new builds. Some projects aren't a good fit for what we do.
Teams who want a free diagnostic, then take it elsewhere
The diagnostic is billable work. A week of an engineer's time reading an unfamiliar codebase, running tests, and writing a thorough assessment is not free. If you proceed with us, the diagnostic fee is credited toward the full engagement.
Systems so badly architected that rescuing costs more than rebuilding
We will tell you this in week 1 if the diagnostic reveals it. Some clients don't want to hear it. If you need us to validate an existing system rather than evaluate it honestly, we're not the right fit.
Anyone who built on a no-code platform and wants custom-code rescue
If your system lives inside Bubble, Voiceflow, Chatbase, or a similar no-code tool, we can't rescue it at the code level, that's not how those platforms work. We build and fix systems in code. If you want to migrate off a no-code platform, that's a rebuild, not a rescue.